{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-sampling-to-reduce-disparate","title":"Active Sampling for Min-Max Fairness","arxiv_id":"2006.06879","date":"2020-06-11","proceeding":null,"authors":["Jacob Abernethy","Pranjal Awasthi","Matthäus Kleindessner","Jamie Morgenstern","Chris Russell","Jie Zhang"],"abstract":"We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model for updating the model. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution.","url_abs":"https://arxiv.org/abs/2006.06879v3","url_pdf":"https://arxiv.org/pdf/2006.06879v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-sampling-to-reduce-disparate","repo_url":"https://github.com/amazon-research/active-sampling-for-minmax-fairness","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.06879","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06879"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amazon-research/active-sampling-for-minmax-fairness","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"ec524753054d77fb","entry":"algorithm_3","repo":"amazon-research/active-sampling-for-minmax-fairness","repo_kind":"official","path":"algorithms/algorithm_3.py","file_url":"https://github.com/amazon-research/active-sampling-for-minmax-fairness/blob/HEAD/algorithms/algorithm_3.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ec524753054d77fb"}},{"code_sha256_prefix":"49e9fc8141d285e5","entry":"compute_error","repo":"amazon-research/active-sampling-for-minmax-fairness","repo_kind":"official","path":"algorithms/utils.py","file_url":"https://github.com/amazon-research/active-sampling-for-minmax-fairness/blob/HEAD/algorithms/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"49e9fc8141d285e5"}},{"code_sha256_prefix":"8032b5bfa85fbf5c","entry":"compute_logistic_loss","repo":"amazon-research/active-sampling-for-minmax-fairness","repo_kind":"official","path":"algorithms/utils.py","file_url":"https://github.com/amazon-research/active-sampling-for-minmax-fairness/blob/HEAD/algorithms/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8032b5bfa85fbf5c"}},{"code_sha256_prefix":"7465410c35c5dc89","entry":"expit_b","repo":"amazon-research/active-sampling-for-minmax-fairness","repo_kind":"official","path":"algorithms/utils.py","file_url":"https://github.com/amazon-research/active-sampling-for-minmax-fairness/blob/HEAD/algorithms/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7465410c35c5dc89"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}